DR‐NAP: Data reduction strategy using neural adaptation phenomenon in wireless sensor networks. (14th March 2023)

• Record Type: Journal Article
• Authors: Kavitha, A.; Guravaiah, Koppala; Leela Velusamy, R.; Suseela, S.; Kumar, Dhilip
• Is Part Of: International journal of communication systems. Volume 36:Number 8(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-03-14
• Subjects: data reduction -- energy‐efficient routing -- Internet of Things -- neural adaptation -- wireless sensor networks -- WSN; Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.5467
• Languages: English
• ISSNs: 1074-5351

Abstract

Summary: Internet of Things (IoT) has got significant popularity among the researchers' community as they have been applied in numerous application domains. Most of the IoT applications are implemented with the help of wireless sensor networks (WSNs). These WSNs use different sensor nodes with a limited battery power supply. Hence, the energy of the sensor node is considered as one of the primary constraints of WSN. Besides, data communication in WSN dissipates more energy than processing the data. In most WSNs applications, the sensed data generated from the same location sensor nodes are identical or time‐series/periodical data. This redundant data transmission leads to more energy consumption. To reduce the energy consumption, a data reduction strategy using neural adaptation phenomenon (DR‐NAP) has been proposed to decrease the communication energy in routing data to the BS in WSN. The neural adaptation phenomenon has been utilized for designing a simple data reduction scheme to decrease the amount of data transmitted. In this way, the sensor node energy is saved and the lifetime of the network is enhanced. The proposed approach has been implanted in the existing gravitational search algorithm (GSA)‐based clustered routing for WSN. The sensed data are transmitted to CH and BS using DR‐NAP. Real sensor data from the Intel Berkeley Research lab have been used for conducting the experiments. The experiment results show 47.82% and 51.96% of improvement in network lifetime when compared with GSA‐based clustered routing and clustering scheme using Canada Geese Migration Principle (CS‐CGMP) for routing, respectively. Abstract : ✓ Adoption of Neural Adaptation Phenomenon for data reduction in order to achieve energy efficiency ✓Integration of Data Reduction strategy using Neural Adaptation Phenomenon (DR‐NAP) approach with Gavitational Search Algorithm (GSA) based clustered routing. ✓ Network lifetime enhancement through reduced data communication.